Debug

1. MV1P
   1. 1. Check the datasets
   2. 2. Check the calibration
   3. 3. Check the triangulation results
   4. 4. Check the output parameter
   5. 5. Check the fitting with SMPL
   6. 6. Check the fitting results

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MV1P

1. Check the datasets

Make sure that:

1. The dataset is synchronized
2. The 2D detections are almost right

# use the annotation tool to check the 2D keypoints
python3 apps/annotation/annot_keypoints.py ${data}
# check the dataset
python3 apps/fit/test_dataset.py --cfg_data config/data/mv1p.yml --opt_data args.path ${data} args.out ${data}/output-keypoints3d

2. Check the calibration

• Make sure that the unit of extrinsic parameter is meter.
• Use check_calib.py to visualize the camera:

# this command will plot a unit cube in the origin:
python3 apps/calibration/check_calib.py ${data} --out ${data} --mode cube --write --show
# this command will read and triangulate the keypoints of the human and project it to each views.
python3 apps/calibration/check_calib.py ${data} --out ${data} --mode human --write --show

3. Check the triangulation results

The results will be stored at ${data}/output-keypoints3d

4. Check the output parameter

1. If the shape is abnormal, check the shapes parameters.

5. Check the fitting with SMPL

# check only first 1 frames
python3 apps/demo/mocap.py ${data} --mode smpl-3d --ranges 0 1 1 --exp debug1
# check only first 10 frames
python3 apps/demo/mocap.py ${data} --mode smpl-3d --ranges 0 10 1 --exp debug10
# check only first 100 frames
python3 apps/demo/mocap.py ${data} --mode smpl-3d --ranges 0 100 1 --exp debug100

6. Check the fitting results

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